Interview Kickstart has enabled over 3500 engineers to uplevel.
Data Analyst, Data Scientist, Software Engineer, Big Data Engineer, Machine Learning Engineer, Machine Learning Scientist–the world of ML and Data Science has a myriad of roles.
Do the names seem similar and add to your confusion?
Helping you through Interview Kickstart brings a series of comparison articles based on similar yet different professions.
Generally dealing with computer, mathematics, and statistics background, there lies vast difference in each field known only to the individuals with knowledge and information. This article deals with the difference between data scientists and machine learning engineers.
Here’s what we’ll cover:
A Data Scientist is a professional who uses technologies to convert raw data into actionable insights and for the process of decision-making. They utilize statistical concepts, data mining, machine learning, and predictive analytics to aid in pattern and trend identification and anomaly detection. The services are available to a wide number of industries, allowing job opportunities for individuals holding expertise in different domains.
A Machine Learning Engineer is a professionally trained individual who works to develop models for prediction-based results. They develop, optimize, and maintain algorithms that are further trained to encounter problems based on the training data. They serve as an important part of the data science team, and their actions are among the prime contributions to the development of Artificial Intelligence Systems.
Distinct professions come with distinct roles and responsibilities, which are stated separately for each.
The different sets of skills required for each profession are stated below.
Knowledge: Must be well-versed in advanced mathematics and statistics. It includes linear algebra, area of calculus, and Bayesian statistics.
Degree: Advanced degree in computer science, mathematics, artificial intelligence, statistics, deep learning, or other relevant field.
Machine Learning: Practical understanding of Machine Learning frameworks, packages, and libraries. Understanding and ability to develop software architecture, data structure, and data modeling.
Soft skills: Communication and problem-solving skills, time management, teamwork, and domain knowledge.
Degree: Bachelor’s and Master’s degree in data science, computer science, statistics, math, or related field
Programming languages: Working experience with programming languages like Python or R and database query languages like SQL, Pig, Hive, C++, Java, or Scala.
Knowledge: Application ability of distributions, regression, statistical tests, and maximum likelihood estimators, specifically to deal with data for results. Strong mathematical skills in multivariate calculus and linear algebra to handle algorithm optimization techniques.
Machine Learning: Knowledge of Machine Learning methods such as Naive Bayes, Decision Forests, K-nearest neighbors, and Support Vector Machines.
Data wrangling: Ability to handle data imperfections and make them worthy of use
Data visualization and communication: Must be proficient in using ggplot, d3.js, matplotlib, and Tableau for visually encoding data. Additionally, one must be able to communicate the understanding to both technical and non-technical audiences.
Soft skills: Must have hands-on experience with tools, problem-solving abilities, and a strong technical background.
Owing to a few similarities in skills, roles, and responsibilities, both professionals deal with some similar and some unique tools. Here is the comprehensive list of both.
The common requirements are:
The insights into salary breakup for both professions are as follows:
Both professions have an increasing demand owing to technological advancements and efforts in the development of AI. The difference lies in the area of focus. A data scientist is concerned more with exploration and data analysis for extracting insights and informed decision-making. On the other hand, a Machine Learning engineer emphasizes the deployment of Machine Learning models to ensure efficiency, scalability, and seamless integration with software systems.
Choosing any of the two fields depends on one’s career goals, prior experience, and other factors. However, preparation requires knowledge from industry experts and personalized guidance. Enroll in our Data Science Masterclass if you are aiming for a data scientist position. Bag the opportunity to learn from Principal and Research Data Scientists from tier-1 companies.
If you want to make it big in the field of Machine Learning, look no further than our foolproof interview prep strategy taught by FAANG engineers. Enroll now in the Machine Learning Course by Interview Kickstart!
Ans. Machine Learning Engineers benefit from a strong foundation in Data Science for data manipulation, analysis, and preprocessing. However, learning it first is not mandatory.
Ans. The need to learn one before another depends on the requirements of the project one is dealing with, career goals, or other similar reasons. Learning Data Science first provides an understanding of data manipulation, visualization, and statistical analysis, while learning Machine Learning first provides insights into predictive modeling and algorithmic aspects.
Ans. The roles are different and complementary. However, replacement with each other is out of the question.
Ans. Machine Learning Engineer tops the list of Best Jobs in 2023 from Indeed. The US has also listed Machine Learning Engineer’s jobs to be at the top compared to that of Data Scientists, making it a more demanding option.
Ans. Yes, it is possible to switch careers from Data Scientist to Machine Learning Engineer. It can be done by filling in the knowledge and skill gap through preparation from online sources or other educational institutions.
Ans. The comparison among each of these is
ML Engineer vs Data Scientist: The ML Engineers deploy and optimize Machine Learning models while Data Scientists emphasize Exploratory Data Analysis.
ML Scientist vs ML Engineer: ML Scientists are concerned with the R&D of novel algorithms, and ML Engineers are concerned with the implementation and scaling of these algorithms.
Data Scientist vs ML Engineer: Data Scientists analyze and interpret complex datasets for decision-making while ML Engineers design and integrate ML models.
Attend our webinar on
"How to nail your next tech interview" and learn